Across every sector, expectations of data professionals are shifting. Organisations are accelerating their investment in data and AI, as 98% expect to increase spending through 2025–26.
Yet the capability to translate analytical work into organisational value has not expanded at the same pace. A structural gap persists. Teams are producing more models, dashboards and analyses than ever, but many still struggle to explain why those outputs matter. Gartner reports that 64% of data leaders find it difficult to communicate the value of their function to executives.
This disconnect limits both the influence of data functions and the progression of the individuals working within them.
At the same time, the skills required for the future are changing. Analytical thinking, AI and Big Data, and leadership and social influence all appear in the top ten growing skills through 2027. Technical capability remains essential, but it is no longer sufficient for those seeking to guide decision-making or operate effectively across functions.
“We have a problem with people with a technical background who don’t know how to translate that technical, data-driven, insightful information for the benefit of the business. ” — Dr Ali Al-Sherbaz, Assistant Professor and Academic Director for Digital Skills courses at University of Cambridge Professional and Continuing Education
As highlighted at a recent event featuring Cambridge faculty and industry experts, one message stood out: today’s most valued data professionals are those who can combine strong analytical skills with effective communication, leadership and business awareness.
Bridging the gap: why technical skills alone don’t guarantee impact
Despite the rapid growth of data functions, organisations still report disappointing returns from analytics initiatives. Two-thirds of analytics projects fail to deliver ROI due to weak stakeholder alignment rather than flawed modelling.
The Institute of Analytics (2025) also highlights communication, adaptability and empathy as top differentiators for data professionals.
Many practitioners are technically strong. They can build models, automate reporting and deliver sophisticated analysis. Yet their work often remains unused or misunderstood.
“Your experience in understanding the data or AI is not useful outside of your comfort zone. That’s where the human element is important.” — Dr Ali Al-Sherbaz
This has become the new measure of success: Do you have the influence needed to turn analysis into action and insight into decisions?
Lessons from leaders: the human skills that amplify technical impact
Organisations that build effective collaboration between data and business teams are 2.4 times more likely to report measurable ROI. Demand for these capabilities is also rising, with a 22% year-over-year rise in leadership and social influence skills across data and AI roles.
For many practitioners, this becomes evident through experience. Parandzem Sargsyan, Lead Data Analyst at ServiceTitan Armenia and Career Accelerator Alumna, reflected on her own development: “At the beginning of my career, I really thought everything was about technical skills… Then I realised that many of these dashboards or models just stay on the shelf. You deliver them, they’re perfect, you’ve spent lots of time and effort doing the analysis, but in the end, they’re not used.”
Her point is straightforward: strong analysis is only one part of the work. Influence comes from understanding the context, engaging with stakeholders and refining insights based on feedback.
“It’s only, like, 30% of the work. The remaining 70% is really understanding the pain point, the problem, and then discussing the results with stakeholders, coming back, adjusting your calculations, and then going and discussing more. So it’s a back-and-forth communication, and you need to be ready for that.”
This pattern is visible in leadership hiring, too. Kate McDermott, Associate Director of Data & AI at Omnis Partners, noted: “The ability to translate the technical complexities of work that’s being done into business impact is key, and that is the differentiator. People who can demonstrate and showcase how they’ve navigated change and transformation within a business, and supported that with technical work.”
A three-pillar leadership model
Across the discussion, three capabilities emerged as defining modern data leadership:
- Technical mastery: advanced modelling, cloud workflows, MLOps and responsible AI.
- Strategic alignment: the ability to connect data work with commercial priorities.
- Stakeholder engagement: clear storytelling, influence and trust-building.
These pillars bridge the gap between “doing data” and “driving outcomes”. They also reflect the Career Accelerator’s focus on technical, strategic, and communication competencies — preparing professionals to progress from practitioner to strategic influencer.
How organisations are redefining data roles
Organisations are rethinking how data functions operate. Rather than positioning data as a support service, many are integrating it as a strategic partner in decision-making. This shift is bringing data teams closer to the centre of organisational discussions, not just the technical delivery process.
Dr Ali Al-Sherbaz described this integration when reflecting on industry collaboration during programme development: “Through that journey [the Employer Project], they learn how they can communicate their business problem through the skills they’ve learned on the technical side…”
This emphasis on end-to-end contribution is also shaping hiring expectations. Kate McDermott noted: “The way you frame the work on a CV or in an interview… is important. There's real value and benefit if you can demonstrate that you can do the end-to-end work. If you can, give me tangible examples on a CV, or in a conversation, or in an actual interview, that will make you stand out from the crowd.”
These insights reflect a broader industry trend. 58% of Chief Data Officers say their biggest hurdle is influencing business culture, signalling a continued need for data professionals who can work fluently across technical and organisational boundaries.
Developing strategic data decision-makers: practical lessons from the panel
When exploring how practitioners can build towards leadership roles, the panel emphasised the value of applied, cross-functional learning.
Parandzem Sargsyan noted the importance of shifting from output to influence:
“You need to be able to deliver your calculations, the technical stuff, in a human way… So, to be able to explain so that everyone understands it...”
Industry research supports the value of practical learning pathways, as communication and adaptability rank among the strongest predictors of promotion.
“{In this programme} they learn not only the technical side, the academic side, but the business side, how can they communicate.” — Dr Ali Al-Sherbaz
Professional growth in data science now requires balancing what you know with how you apply and communicate it.
Where technical mastery meets strategic influence
Becoming a data-science leader in 2026 does not mean stepping away from technical work. It means elevating its impact.
Professionals who blend advanced analytical skills with communication, strategy and leadership will define the next generation of data-driven decision-makers.
The Cambridge PACE Data Science With Machine Learning & AI Career Accelerator, delivered in collaboration with FourthRev, embodies this philosophy — blending advanced data-science practice with applied projects, mentorship, and the development of storytelling and stakeholder-influence skills that prepare learners to drive real business impact.